In this paper we introduce a general framework for defining the depth of a sequence with respect to a class of observers. We show that our general framework captures all depth not...
The electronic sharing of general scientific data can be a complex but incredibly beneficial process. We identify elements that must exist in a system designed for such a purpose ...
Abstract. This paper examines the generalization capability in learning multiple temporal patterns by the recurrent neural network with parametric bias (RNNPB). Our simulation expe...
In this paper we present the geometrical construction of an approximate generalized Voronoi diagram for generalized polygons and circular objects based on their minimum geometrica...
We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the test...